LLM-Augmented Deep Reinforcement Learning for Dynamic O-RAN Network Slicing

Advanced wireless networks must be highly dynamic and capable of managing heterogeneous service demands. A key feature of these networks is network slicing, which is supported in the next-generation radio access network (RAN) architecture, such as open RAN (O-RAN), by leveraging artificial intelligence (AI) and machine learning (ML) approaches within the ran intelligent controller (RIC) modules. Deep reinforcement learning (DRL) has much potential for managing dynamic networks but often needs to improve when dealing with unstructured, multi-modal data like RF signals and QoS metrics. This kind of data makes it challenging for DRL to grasp the high-level context fully. To tackle this, we're using large language models (LLMs) to give DRL a richer, more meaningful state representation. LLMs add semantic layers to raw data, helping the agent grasp deeper context and think more strategically over the long term. This way, the DRL agent can make smarter decisions even as the environment becomes more complex and changes over time. This paper proposes a task-related state representation that employs LLM to augment multi-agent DRL (MARL) approaches. Simulation results demonstrate that the proposed approach significantly outperforms related baselines.